Calculating the Topological Resilience of Supply Chain Networks Using Hopfield Neural Networks

The resilience of supply networks in a manufacturer's operations has always been an important topic, and has drawn increasing attention in the modern world of lean processes, unpredictable disruptions, and high expectations for consistent financial returns. There is little agreement in the scientific literature on how the resilience of a complex global supply network should be calculated. This disagreement extends from the modeling and optimization space to real-time event monitoring to projecting impacts and executing corrective actions. One type of resilience, that is of interest, is topological resilience, i.e. how is the downtime of a network node going to affect the network flow. In this paper, we propose a machine learning algorithm, based on using Hopfield Neural Nets as optimizers, to calculate one type of topological resilience for a supply chain network. Namely, we present an algorithm to determine whether there is a subset of nodes which, if they go down simultaneously, will render the supply chain non-operational, causing a catastrophic failure of the whole system. The stated problem is NP-complete by its nature.